COIN-GP: Cooperative Online Learning in Networked Distributed Systems with Partial Measurements via Gaussian Process Regression

The paper introduces COIN-GP, an observer‑based cooperative learning framework for distributed sensor networks that jointly estimates system states and partially unknown dynamics using online distributed Gaussian Process regression. It addresses challenges of incomplete state observations and deficient GP models by proposing a novel data collection strategy with theoretical feasibility conditions. The authors also derive an error upper bound for both state and model estimation and demonstrate through simulations that COIN‑GP outperforms existing distributed GP‑based methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.